Data Mining for Systems Biology
Data Mining for Systems Biology

Author: Hiroshi Mamitsuka

Publisher: Humana

Published: 2019-08-04

Total Pages: 243

ISBN-13: 9781493993260

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This fully updated book collects numerous data mining techniques, reflecting the acceleration and diversity of the development of data-driven approaches to the life sciences. The first half of the volume examines genomics, particularly metagenomics and epigenomics, which promise to deepen our knowledge of genes and genomes, while the second half of the book emphasizes metabolism and the metabolome as well as relevant medicine-oriented subjects. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that is useful for getting optimal results. Authoritative and practical, Data Mining for Systems Biology: Methods and Protocols, Second Edition serves as an ideal resource for researchers of biology and relevant fields, such as medical, pharmaceutical, and agricultural sciences, as well as for the scientists and engineers who are working on developing data-driven techniques, such as databases, data sciences, data mining, visualization systems, and machine learning or artificial intelligence that now are central to the paradigm-altering discoveries being made with a higher frequency.

Biological Data Mining
Biological Data Mining

Author: Jake Y. Chen

Publisher: CRC Press

Published: 2009-09-01

Total Pages: 736

ISBN-13: 1420086855

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Like a data-guzzling turbo engine, advanced data mining has been powering post-genome biological studies for two decades. Reflecting this growth, Biological Data Mining presents comprehensive data mining concepts, theories, and applications in current biological and medical research. Each chapter is written by a distinguished team of interdisciplin

Advanced Data Mining Technologies in Bioinformatics
Advanced Data Mining Technologies in Bioinformatics

Author: Hui-Huang Hsu

Publisher: IGI Global

Published: 2006-01-01

Total Pages: 343

ISBN-13: 1591408636

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"This book covers research topics of data mining on bioinformatics presenting the basics and problems of bioinformatics and applications of data mining technologies pertaining to the field"--Provided by publisher.

Computational Systems Biology
Computational Systems Biology

Author: Andres Kriete

Publisher: Academic Press

Published: 2013-11-26

Total Pages: 549

ISBN-13: 0124059384

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This comprehensively revised second edition of Computational Systems Biology discusses the experimental and theoretical foundations of the function of biological systems at the molecular, cellular or organismal level over temporal and spatial scales, as systems biology advances to provide clinical solutions to complex medical problems. In particular the work focuses on the engineering of biological systems and network modeling. - Logical information flow aids understanding of basic building blocks of life through disease phenotypes - Evolved principles gives insight into underlying organizational principles of biological organizations, and systems processes, governing functions such as adaptation or response patterns - Coverage of technical tools and systems helps researchers to understand and resolve specific systems biology problems using advanced computation - Multi-scale modeling on disparate scales aids researchers understanding of dependencies and constraints of spatio-temporal relationships fundamental to biological organization and function.

Biological Data Mining in Protein Interaction Networks
Biological Data Mining in Protein Interaction Networks

Author: Li, Xiao-Li

Publisher: IGI Global

Published: 2009-05-31

Total Pages: 448

ISBN-13: 1605663999

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"The goal of this book is to disseminate research results and best practices from cross-disciplinary researchers and practitioners interested in, and working on bioinformatics, data mining, and proteomics"--Provided by publisher.

Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics

Author: Elena Marchiori

Publisher: Springer Science & Business Media

Published: 2007-04-02

Total Pages: 311

ISBN-13: 354071782X

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This book constitutes the refereed proceedings of the 5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2007, held in Valencia, Spain, April 2007. Coverage brings together experts in computer science with experts in bioinformatics and the biological sciences. It presents contributions on fundamental and theoretical issues along with papers dealing with different applications areas.

Life Science Data Mining
Life Science Data Mining

Author: Stephen T. C. Wong

Publisher: World Scientific Publishing Company

Published: 2006

Total Pages: 392

ISBN-13:

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This timely book identifies and highlights the latest data mining paradigms to analyze, combine, integrate, model and simulate vast amounts of heterogeneous multi-modal, multi-scale data for emerging real-world applications in life science.The cutting-edge topics presented include bio-surveillance, disease outbreak detection, high throughput bioimaging, drug screening, predictive toxicology, biosensors, and the integration of macro-scale bio-surveillance and environmental data with micro-scale biological data for personalized medicine. This collection of works from leading researchers in the field offers readers an exceptional start in these areas.

Elements of Computational Systems Biology
Elements of Computational Systems Biology

Author: Huma M. Lodhi

Publisher: John Wiley & Sons

Published: 2010-03-25

Total Pages: 435

ISBN-13: 0470556749

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Groundbreaking, long-ranging research in this emergent field that enables solutions to complex biological problems Computational systems biology is an emerging discipline that is evolving quickly due to recent advances in biology such as genome sequencing, high-throughput technologies, and the recent development of sophisticated computational methodologies. Elements of Computational Systems Biology is a comprehensive reference covering the computational frameworks and techniques needed to help research scientists and professionals in computer science, biology, chemistry, pharmaceutical science, and physics solve complex biological problems. Written by leading experts in the field, this practical resource gives detailed descriptions of core subjects, including biological network modeling, analysis, and inference; presents a measured introduction to foundational topics like genomics; and describes state-of-the-art software tools for systems biology. Offers a coordinated integrated systems view of defining and applying computational and mathematical tools and methods to solving problems in systems biology Chapters provide a multidisciplinary approach and range from analysis, modeling, prediction, reasoning, inference, and exploration of biological systems to the implications of computational systems biology on drug design and medicine Helps reduce the gap between mathematics and biology by presenting chapters on mathematical models of biological systems Establishes solutions in computer science, biology, chemistry, and physics by presenting an in-depth description of computational methodologies for systems biology Elements of Computational Systems Biology is intended for academic/industry researchers and scientists in computer science, biology, mathematics, chemistry, physics, biotechnology, and pharmaceutical science. It is also accessible to undergraduate and graduate students in machine learning, data mining, bioinformatics, computational biology, and systems biology courses.

Translational Bioinformatics and Systems Biology Methods for Personalized Medicine
Translational Bioinformatics and Systems Biology Methods for Personalized Medicine

Author: Qing Yan

Publisher: Academic Press

Published: 2017-04-18

Total Pages: 184

ISBN-13: 0128043881

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Translational Bioinformatics and Systems Biology Methods for Personalized Medicine introduces integrative approaches in translational bioinformatics and systems biology to support the practice of personalized, precision, predictive, preventive, and participatory medicine. Through the description of important cutting-edge technologies in bioinformatics and systems biology, readers may gain an essential understanding of state-of-the-art methodologies. The book discusses topics such as the challenges and tasks in translational bioinformatics; pharmacogenomics, systems biology, and personalized medicine; and the applicability of translational bioinformatics for biomarker discovery, epigenomics, and molecular dynamics. It also discusses data integration and mining, immunoinformatics, and neuroinformatics. With broad coverage of both basic scientific and clinical applications, this book is suitable for a wide range of readers who may not be scientists but who are also interested in the practice of personalized medicine. Introduces integrative approaches in translational bioinformatics and systems biology to support the practice of personalized, precision, predictive, preventive, and participatory medicine Presents a problem-solving oriented methodology to deal with practical problems in various applications Covers both basic scientific and clinical applications in order to enhance the collaboration between researchers and clinicians Brings integrative and multidisciplinary approaches to bridge the gaps among various knowledge domains in the field

Biological Data Mining And Its Applications In Healthcare
Biological Data Mining And Its Applications In Healthcare

Author: Xiaoli Li

Publisher: World Scientific

Published: 2013-11-28

Total Pages: 437

ISBN-13: 9814551023

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Biologists are stepping up their efforts in understanding the biological processes that underlie disease pathways in the clinical contexts. This has resulted in a flood of biological and clinical data from genomic and protein sequences, DNA microarrays, protein interactions, biomedical images, to disease pathways and electronic health records. To exploit these data for discovering new knowledge that can be translated into clinical applications, there are fundamental data analysis difficulties that have to be overcome. Practical issues such as handling noisy and incomplete data, processing compute-intensive tasks, and integrating various data sources, are new challenges faced by biologists in the post-genome era. This book will cover the fundamentals of state-of-the-art data mining techniques which have been designed to handle such challenging data analysis problems, and demonstrate with real applications how biologists and clinical scientists can employ data mining to enable them to make meaningful observations and discoveries from a wide array of heterogeneous data from molecular biology to pharmaceutical and clinical domains.